Count

Estimated Number of Cells

Median Gene per Cell

Mean Reads per Cell

Median UMI per Cell

Cells

Estimated Number of Cells

The number of barcodes associated with at least one cell.

Confidently Mapped Reads in Cells

The number of mapped reads that are assigned to valid cell barcodes.

UMIs in Cells

The proportion of UMIs assigned to valid cells to the total UMIs.

Mean Reads per Cell

The average number of mapped reads per cell detected.

Median Reads per Cell

The median number of unique mapped reads per cell detected.

Mean UMI per Cell

The average number of UMIs per cell detected.

Median UMI per Cell

The median number of UMIs per cell detected.

Mean Gene per Cell

The average number of Genes per cell detected.

Median Gene per Cell

The median number of Genes per cell detected.

Total Gene Detected

The number of genes with at least one UMI count in cell detected.

Estimated Number of Cells	{{Estimated Number of Cells}}
Confidently Mapped Reads in Cells	{{Confidently Mapped Reads in Cells}}
UMIs in Cells	{{UMIs in Cells}}
Mean Reads per Cell	{{Mean Reads per Cell}}
Median Reads per Cell	{{Median Reads per Cell}}
Mean UMI per Cell	{{Mean UMI per Cell}}
Median UMI per Cell	{{Median UMI per Cell}}
Mean Gene per Cell	{{Mean Gene per Cell}}
Median Gene per Cell	{{Median Gene per Cell}}
Total Gene Detected	{{Total Gene Detected}}

Sequencing_saturation

Left

This plot shows the Sequencing Saturation metric as a function of downsampled sequencing depth (measured in mean reads per cell), up to the observed sequencing depth. Sequencing Saturation is a measure of the observed library complexity, and approaches 1.0 (100%) when all converted mRNA transcripts have been sequenced. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point. The dotted line is drawn at a value reasonably approximating the saturation point.

Right

This plot shows the Median Genes per Cell as a function of downsampled sequencing depth in mean reads per cell, up to the observed sequencing depth. The slope of the curve near the endpoint can be interpreted as an upper bound to the benefit to be gained from increasing the sequencing depth beyond this point.

Summary

Number of Reads

The number of total raw reads.

Reads With Valid Barcodes

The number and fraction of reads with valid barcodes.

Sequencing Saturation

Sequencing saturation refers to the percentage of reads originating from a duplicate UMI (in other words, an mRNA molecule that was sequenced more than 1 time).

Q30 Bases in CB+UMI

The fraction of cell-barcode and UMI bases with Q-scores >= 30.

Q30 Bases in RNA read

The fraction of RNA read bases with Q-scores >= 30.

Sequencing Saturation Plot

This plot shows the Sequencing Saturation metric as a function of downsampled sequencing depth, up to the observed sequencing depth. A high saturation indicates we are detecting the vast majority of mRNA molecules in the samples, and thus don't need to sequence the libraries any deeper.

Sample	{{Sample}}
Reference	{{Reference}}
Kit	{{Kit}}
Pipeline version	{{Pipeline version}}
Number of Reads	{{Number of Reads}}

Reads With Valid Barcodes	{{Reads With Valid Barcodes}}
Sequencing Saturation	{{Sequencing Saturation}}
Q30 Bases in CB+UMI	{{Q30 Bases in CB+UMI}}
Q30 Bases in RNA read	{{Q30 Bases in RNA read}}

Mapping

Reads Mapped to Genome

The number and fraction of reads mapped to the genome.

Reads Mapped Confidently to Genome

The number and fraction of reads uniquely mapped to the genome.

Reads Mapped Confidently to Transcriptome

The number and fraction of reads uniquely mapped to the transcriptome. The read must be consistent with annotated splice junctions.

Reads Mapped Confidently to Exonic

The number and fraction of reads that uniquely mapped to an exonic region of the genome.

Reads Mapped Confidently to Intronic

The number and fraction of reads that uniquely mapped to an intronic region of the genome.

Reads Mapped Confidently to Intergenic

The number and fraction of reads that uniquely mapped to the intergenic region of the genome.

Reads Mapped to antisense

The number and fraction of reads uniquely mapped to the antisense strand of gene.

Reads Mapped to mito

Fraction of reads that mapped uniquely to mitochondria region of the genome.

Reads Mapped to Genome	{{Reads Mapped to Genome}}
Reads Mapped Confidently to Genome	{{Reads Mapped Confidently to Genome}}
Reads Mapped Confidently to Transcriptome	{{Reads Mapped Confidently to Transcriptome}}
Reads Mapped Confidently to Exonic	{{Reads Mapped Confidently to Exonic}}

Reads Mapped Confidently to Intronic	{{Reads Mapped Confidently to Intronic}}
Reads Mapped Confidently to Intergenic	{{Reads Mapped Confidently to Intergenic}}
Reads Mapped to antisense	{{Reads Mapped to antisense}}
Reads Mapped to mito	{{Reads Mapped to mito}}

Distribution

Gene counts

The distribution of effective gene counts detected in each cell.

UMI counts

The distribution of UMI counts detected in each cell.

Mito percentage

The distribution of mitochondrial fraction detected in each cell.

Cluster

The display is limited to a random subset of cells.

left

This plot shows the UMI counts for each cell barcode. Each dot associated with a cell barcode and is colored by the number of total UMI counts. The coordinate axes represents the 2-dimensional embedding produced by the UMAP(Uniform Manifold Approximation and Projection)algorithm.

right

This plot shows the automated clustering result for each cell-barcode by UMAP algorithm. Each dot associated with a cell barcode and is colored according to different cluster.

{cluster_umap1}

Genes

The table shows the top 30 differentially expressed genes for each cluster. Here a differential expression test was performed between each cluster and the rest of the sample for each feature. The avg_log2FC is an estimate of the log2 ratio of expression in a cluster to that in all other cells. The p_val is a measure of the statistical significance of the expression difference and the p_val_adj is adjusted p-value, based on bonferroni correction using all features in the dataset.